Paper Citation Record · LEDGER

Numerical Investigation of Sequence Modeling Theory using Controllable Memory Functions

As of 9 August 2026, this Paper Citation Record lists 35 of 35 outbound references and 0 inbound Pith citation observations for arXiv:2506.05678.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2506.05678 v3

Coverage vector

measured 35 of 35 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T10:22:22.376764Z

measured 35 of 35 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

35 of 35 outbound references displayed

verified exact0
verified fuzzy23
unresolved12
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation b68a4bac-e5c6-4993-a954-9231b7224328 · outbound

This paper cites @esa (Ref.

Numerical Investigation of Sequence Modeling Theory using Controllable Memory Functions @esa (Ref

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-07T10:22:22.097255Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0a94128c-0481-4b26-b503-6b162425bc99 · outbound

This paper cites an unresolved cited work.

Numerical Investigation of Sequence Modeling Theory using Controllable Memory Functions Unresolved cited work

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-07T10:22:22.106810Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 099e65f9-189c-4db7-a11b-8d7c8a644053 · outbound

This paper cites an unresolved cited work.

Numerical Investigation of Sequence Modeling Theory using Controllable Memory Functions Unresolved cited work

Reference 3

Resolution: unresolved; raw_fallback, observed 2026-08-07T10:22:23.063340Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 18370100-2b0e-42a6-8170-59c36882e105 · outbound

This paper cites Quality over Quantity in Attention Layers : When Adding More Heads Hurts.

Numerical Investigation of Sequence Modeling Theory using Controllable Memory Functions Quality over Quantity in Attention Layers : When Adding More Heads Hurts

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:22:23.040335Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation d3cfa963-6456-4ae0-aab8-d41b03c875f8 · outbound

This paper cites Zico Kolter, and Vladlen Koltun.

Numerical Investigation of Sequence Modeling Theory using Controllable Memory Functions Zico Kolter, and Vladlen Koltun

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:22:23.010873Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 8b3ba32a-9d4d-4f5c-8ed3-05d1a534cc1d · outbound

This paper cites LongBench : A Bilingual , Multitask Benchmark for Long Context Understanding , June 2024.

Numerical Investigation of Sequence Modeling Theory using Controllable Memory Functions LongBench : A Bilingual , Multitask Benchmark for Long Context Understanding , June 2024

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:22:22.984189Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 2b890374-2905-468d-806f-5dfb364dd052 · outbound

This paper cites Bengio, P.

Numerical Investigation of Sequence Modeling Theory using Controllable Memory Functions Bengio, P

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-07T10:22:22.151437Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b859d881-c1fe-4521-a9c6-b078f1ef9c00 · outbound

This paper cites On the Relationship between Self-Attention and Convolutional Layers.

Numerical Investigation of Sequence Modeling Theory using Controllable Memory Functions On the Relationship between Self-Attention and Convolutional Layers

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-07T10:22:22.158470Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d2d84b5b-ea8e-4466-9cba-5acb083895f6 · outbound

This paper cites BAMBOO : A Comprehensive Benchmark for Evaluating Long Text Modeling Capacities of Large Language Models , March 2024.

Numerical Investigation of Sequence Modeling Theory using Controllable Memory Functions BAMBOO : A Comprehensive Benchmark for Evaluating Long Text Modeling Capacities of Large Language Models , March 2024

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:22:22.958236Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation a565ce8e-c803-4517-9f5e-6067f3174fda · outbound

This paper cites The Pile : An 800GB Dataset of Diverse Text for Language Modeling , December 2020.

Numerical Investigation of Sequence Modeling Theory using Controllable Memory Functions The Pile : An 800GB Dataset of Diverse Text for Language Modeling , December 2020

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-07T10:22:22.172765Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4901f98f-d98b-48c8-9db4-c16c35eca769 · outbound

This paper cites Efficiently Modeling Long Sequences with Structured State Spaces , August 2022 a.

Numerical Investigation of Sequence Modeling Theory using Controllable Memory Functions Efficiently Modeling Long Sequences with Structured State Spaces , August 2022 a

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:22:22.924029Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation ebb642d5-47db-4d64-84dc-7d0689815d52 · outbound

This paper cites On the Parameterization and Initialization of Diagonal State Space Models , August 2022 b.

Numerical Investigation of Sequence Modeling Theory using Controllable Memory Functions On the Parameterization and Initialization of Diagonal State Space Models , August 2022 b

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:22:22.903591Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation ccedb0eb-9bc9-416f-9a62-e86810bdc093 · outbound

This paper cites Long Short-Term Memory.

Numerical Investigation of Sequence Modeling Theory using Controllable Memory Functions Long Short-Term Memory

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-07T10:22:22.205180Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a0918d4d-4153-4f72-98cc-6ed91902827d · outbound

This paper cites RULER : What 's the Real Context Size of Your Long-Context Language Models ?, August 2024.

Numerical Investigation of Sequence Modeling Theory using Controllable Memory Functions RULER : What 's the Real Context Size of Your Long-Context Language Models ?, August 2024

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:22:22.884647Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation ec2cb9a1-bc6e-49cf-9853-79c41154b8b2 · outbound

This paper cites Approximation Rate of the Transformer Architecture for Sequence Modeling.

Numerical Investigation of Sequence Modeling Theory using Controllable Memory Functions Approximation Rate of the Transformer Architecture for Sequence Modeling

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:22:22.866874Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 544a491d-0124-429d-a43a-ed1ee378e8fc · outbound

This paper cites Approximation Theory of Convolutional Architectures for Time Series Modelling.

Numerical Investigation of Sequence Modeling Theory using Controllable Memory Functions Approximation Theory of Convolutional Architectures for Time Series Modelling

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:22:22.848694Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation dfbc51c0-b322-4842-a608-98e09e7a30f4 · outbound

This paper cites Krizhevsky.

Numerical Investigation of Sequence Modeling Theory using Controllable Memory Functions Krizhevsky

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:22:22.832559Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 8ed72dbd-923e-4dc4-afd4-6851a78a759d · outbound

This paper cites Can Vision Transformers Perform Convolution ?, November 2021.

Numerical Investigation of Sequence Modeling Theory using Controllable Memory Functions Can Vision Transformers Perform Convolution ?, November 2021

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:22:22.814336Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation d830c431-f00c-453c-a2a7-40f5c427307c · outbound

This paper cites Approximation and Optimization Theory for Linear Continuous-Time Recurrent Neural Networks.

Numerical Investigation of Sequence Modeling Theory using Controllable Memory Functions Approximation and Optimization Theory for Linear Continuous-Time Recurrent Neural Networks

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:22:22.795296Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 52bfaa91-4217-4c10-a409-f19f982c9431 · outbound

This paper cites Maas, Raymond E.

Numerical Investigation of Sequence Modeling Theory using Controllable Memory Functions Maas, Raymond E

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:22:22.777521Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation f31d887c-d201-4a3e-bcec-481c501b5db9 · outbound

This paper cites Pointer Sentinel Mixture Models.

Numerical Investigation of Sequence Modeling Theory using Controllable Memory Functions Pointer Sentinel Mixture Models

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:22:22.760736Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 980e9167-313f-408b-856c-b4076e38be63 · outbound

This paper cites Stable Recurrent Models , March 2019.

Numerical Investigation of Sequence Modeling Theory using Controllable Memory Functions Stable Recurrent Models , March 2019

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:22:22.742579Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 70eb2665-2bf8-4cc4-bc75-12a12b763b33 · outbound

This paper cites an unresolved cited work.

Numerical Investigation of Sequence Modeling Theory using Controllable Memory Functions Unresolved cited work

Reference 23

Resolution: unresolved; raw_fallback, observed 2026-08-07T10:22:22.723593Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation afe50a40-1c05-4bcf-8c25-c65013be4d34 · outbound

This paper cites Smith, Albert Gu, Anushan Fernando, Caglar Gulcehre, Razvan Pascanu, and Soham De.

Numerical Investigation of Sequence Modeling Theory using Controllable Memory Functions Smith, Albert Gu, Anushan Fernando, Caglar Gulcehre, Razvan Pascanu, and Soham De

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:22:22.705369Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 751dee40-3a91-44dc-8183-1bb5c36d180d · outbound

This paper cites The LAMBADA dataset: Word prediction requiring a broad discourse context, June 2016.

Numerical Investigation of Sequence Modeling Theory using Controllable Memory Functions The LAMBADA dataset: Word prediction requiring a broad discourse context, June 2016

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:22:22.685906Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 3ddbf31a-18b9-4253-8590-5067a7b30339 · outbound

This paper cites Rumelhart, Geoffrey E.

Numerical Investigation of Sequence Modeling Theory using Controllable Memory Functions Rumelhart, Geoffrey E

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-07T10:22:22.305146Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c9353c12-f0e1-4a15-9366-a7d419d93038 · outbound

This paper cites Self-Attention with Relative Position Representations.

Numerical Investigation of Sequence Modeling Theory using Controllable Memory Functions Self-Attention with Relative Position Representations

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-07T10:22:22.311167Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e37230f2-b154-4957-8e3c-b994dce8b051 · outbound

This paper cites RoFormer : Enhanced Transformer with Rotary Position Embedding , November 2023.

Numerical Investigation of Sequence Modeling Theory using Controllable Memory Functions RoFormer : Enhanced Transformer with Rotary Position Embedding , November 2023

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:22:22.666508Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 9202fbb5-e5a9-477f-86a7-d46ef51f8678 · outbound

This paper cites Long Range Arena : A Benchmark for Efficient Transformers.

Numerical Investigation of Sequence Modeling Theory using Controllable Memory Functions Long Range Arena : A Benchmark for Efficient Transformers

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:22:22.648082Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation e51cc932-c061-4a8d-94fa-97c38f9e96ab · outbound

This paper cites WaveNet: A Generative Model for Raw Audio.

Numerical Investigation of Sequence Modeling Theory using Controllable Memory Functions WaveNet: A Generative Model for Raw Audio

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-07T10:22:22.337568Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4646a706-03fa-4309-bf79-960faac30586 · outbound

This paper cites Attention is All you Need.

Numerical Investigation of Sequence Modeling Theory using Controllable Memory Functions Attention is All you Need

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-07T10:22:22.344874Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e2587cf0-da76-4096-b171-88fb3e5bf9a9 · outbound

This paper cites StableSSM : Alleviating the Curse of Memory in State-space Models through Stable Reparameterization.

Numerical Investigation of Sequence Modeling Theory using Controllable Memory Functions StableSSM : Alleviating the Curse of Memory in State-space Models through Stable Reparameterization

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:22:22.600924Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 12027641-4e77-422c-aa6d-5e5438d2749d · outbound

This paper cites State-space models with layer-wise nonlinearity are universal approximators with exponential decaying memory.

Numerical Investigation of Sequence Modeling Theory using Controllable Memory Functions State-space models with layer-wise nonlinearity are universal approximators with exponential decaying memory

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:22:22.581320Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 7b0140f6-122d-4e64-8ff1-a21658262c68 · outbound

This paper cites Inverse Approximation Theory for Nonlinear Recurrent Neural Networks.

Numerical Investigation of Sequence Modeling Theory using Controllable Memory Functions Inverse Approximation Theory for Nonlinear Recurrent Neural Networks

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:22:22.561663Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation e75872e5-e021-4b51-b5f9-1150409fc43d · outbound

This paper cites Do RNN and LSTM have Long Memory ? In Proceedings of the 37th International Conference on Machine Learning , pp.\ 11365--11375.

Numerical Investigation of Sequence Modeling Theory using Controllable Memory Functions Do RNN and LSTM have Long Memory ? In Proceedings of the 37th International Conference on Machine Learning , pp.\ 11365--11375

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:22:22.544755Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Pith citing papers

No inbound Pith citation observations are available.